Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain su...Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.展开更多
Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is pro...Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.展开更多
The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components(e...The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components(e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms(ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms(DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables(ADVs) and dynamic threshold parameters(DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.展开更多
Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular...Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular computational networks(CCNs)structure.The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture.In the prediction layer,CCNs are employed to reduce the system scale by considering only relevant generators.In the correction layer,a novel adaptive filter is utilized to increase data abundance.Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency.Additionally,it offers easy scalability and strong robustness against uncertainties.The proposed method has potential applications in online dynamic state estimation and real-time security monitoring.展开更多
The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault character...The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion.Focusing on large-scale renewable energy transmission lines,the sensitivity of traditional current differential protection and distance protection may be reduced,and there is even the risk of maloperation.Therefore,a suitable transmission line model is established,which considers the distributed capacitance.Af terward,a novel dynamic state estimation based protection(DSEBP)for large-scale renewable energy transmission lines is proposed.The proposed DSEBP adopts instantaneous measure ments and additional protection criteria to ensure the quick ac tion and reliability.Finally,faults are identified by checking the matching degree between the actual measurements and the es tablished transmission line model.The performance of the pro posed DSEBP is verified through PSCAD/EMTDC and realtime digital simulator(RTDS)hardware-in-loop tests.The re sults demonstrate that the proposed DSEBP can identify vari ous types of faults quickly and reliably.Meanwhile,the pro posed DSEBP has a better capability to withstand fault resis tance and disturbance.展开更多
The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledg...The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.展开更多
The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic ext...The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.展开更多
This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation sys...This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.展开更多
In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to es...In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.展开更多
Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power syst...Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.展开更多
In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first...In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.展开更多
High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges ar...High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.展开更多
A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the d...A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.展开更多
In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the ...In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.展开更多
Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG...Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.展开更多
The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical appro...The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.展开更多
This study analyzes the role of financial development(FD)on the impact of technologi-cal innovation(TI)on six environmental quality indicators for the 25 economies that are part of the Organization for Economic Cooper...This study analyzes the role of financial development(FD)on the impact of technologi-cal innovation(TI)on six environmental quality indicators for the 25 economies that are part of the Organization for Economic Cooperation and Development for the period from 2000 to 2019.We use a two-step dynamic generalized method of moments approach to understand this relationship.The results show that FD augments the posi-tive effects of TI on four of the six environmental indicators,namely ecological foot-print,adjusted net savings,pressure on nature,and environmental performance.However,no significant effects on environmental sustainability and environmental vulnerability indices were found.When considering all of the environmental quality indicators,TI appears to enhance environmental quality.We find evidence to support the existence of the environmental Kuznets curve in the context of each environmen-tal indicator and economic growth.Moreover,FD and energy consumption appear to accelerate environmental degradation.Based on these results,FD should be viewed as an important parameter in designing policies for innovation to achieve the goal of net-zero carbon emissions.Highlights.Technological innovation and environmental quality nexus is studied.The moderating role of financial development is analyzed.Six different environmental quality indicators are used for OECD countries.Financial development intensifies the environmental benefits of innovation.•The EKC hypothesis is confirmed for all six environmental indicators.展开更多
This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric com...This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.展开更多
The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demand...The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.展开更多
With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they se...With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 62071345。
文摘Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.
文摘Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.
基金supported by the Australian Research Council Discovery Early Career Researcher Award(No.DE200101128).
文摘The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components(e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms(ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms(DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables(ADVs) and dynamic threshold parameters(DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.
基金supported in part by the National Natural Science Foundation of China(No.62203395)in part by the China Postdoctoral Science Foundation(No.2023TQ0306)+1 种基金in part by the Natural Science Foundation of Henan Province(No.242300421167)in part by the Postdoctoral Research Project of Henan Province(No.202101011).
文摘Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular computational networks(CCNs)structure.The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture.In the prediction layer,CCNs are employed to reduce the system scale by considering only relevant generators.In the correction layer,a novel adaptive filter is utilized to increase data abundance.Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency.Additionally,it offers easy scalability and strong robustness against uncertainties.The proposed method has potential applications in online dynamic state estimation and real-time security monitoring.
基金supported by National Natural Science Foundation of ChinaState Grid Corporation Joint Fund for Smart Grid(No.U2066210).
文摘The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion.Focusing on large-scale renewable energy transmission lines,the sensitivity of traditional current differential protection and distance protection may be reduced,and there is even the risk of maloperation.Therefore,a suitable transmission line model is established,which considers the distributed capacitance.Af terward,a novel dynamic state estimation based protection(DSEBP)for large-scale renewable energy transmission lines is proposed.The proposed DSEBP adopts instantaneous measure ments and additional protection criteria to ensure the quick ac tion and reliability.Finally,faults are identified by checking the matching degree between the actual measurements and the es tablished transmission line model.The performance of the pro posed DSEBP is verified through PSCAD/EMTDC and realtime digital simulator(RTDS)hardware-in-loop tests.The re sults demonstrate that the proposed DSEBP can identify vari ous types of faults quickly and reliably.Meanwhile,the pro posed DSEBP has a better capability to withstand fault resis tance and disturbance.
基金National Natural Science Foundation of China (60572023)
文摘The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.
基金supported by the National Natural Science Foundation of China(41504115)the Shaanxi Province Natural Science Foundation(2015JQ6223)+2 种基金the Foundation of Strengthening Police Science and Technology from Ministry of Public Security(2015GABJC50)the International Technology Cooperation Plan Project of Shaanxi Province(2015KW-0142015KW-013)
文摘The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.
基金supported by the National Natural Science Foundation of China(No.62073121)the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202)+1 种基金the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004)the Natural Sciences and Engineering Research Council(NSERC)of Canada。
文摘This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.
基金This work was supported in part by National Natural Science Foundation of China(51777067)and(52077076)in part by funding from the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(LAPS2021-18).
文摘In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.
基金supported by the Electric Power Research Institute and also makes use of Engineering Research Center Shared Facilities supported by the DOE under U.S.NSF Award Number EEC1041877support is provided by the U.S.CURENT Industry Partnership Program and China National Government Building Highlevel University Graduate Programs([2012]3013).
文摘Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.
基金supported by the National Natural Science Foundation of China(No.62073121)the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202)the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004)。
文摘In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.
基金This work is supported by Electric Power Research Institute(EPRI).Its support is greatly appreciated.
文摘High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.
基金This work was supportedbytheNationalNaturalScienceFoundationofChina(No.60474051),theProgramforNewCenturyExcellentTalentsinUniversityofChina(NCET),andtheSpecializedResearchFundfortheDoctoralProgramofHigherEducationofChina(No.20020248028).
文摘A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.
文摘In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.
基金supported by the National Natural Science Foundation of China(No.51725702)。
文摘Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.
基金supported by the National Key Basic Research Program of China(973 Program)(No.613294)the Natural Science Foundation of China(No.51877211)
文摘The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.
基金This research paper did not receive any financial aid from any source.
文摘This study analyzes the role of financial development(FD)on the impact of technologi-cal innovation(TI)on six environmental quality indicators for the 25 economies that are part of the Organization for Economic Cooperation and Development for the period from 2000 to 2019.We use a two-step dynamic generalized method of moments approach to understand this relationship.The results show that FD augments the posi-tive effects of TI on four of the six environmental indicators,namely ecological foot-print,adjusted net savings,pressure on nature,and environmental performance.However,no significant effects on environmental sustainability and environmental vulnerability indices were found.When considering all of the environmental quality indicators,TI appears to enhance environmental quality.We find evidence to support the existence of the environmental Kuznets curve in the context of each environmen-tal indicator and economic growth.Moreover,FD and energy consumption appear to accelerate environmental degradation.Based on these results,FD should be viewed as an important parameter in designing policies for innovation to achieve the goal of net-zero carbon emissions.Highlights.Technological innovation and environmental quality nexus is studied.The moderating role of financial development is analyzed.Six different environmental quality indicators are used for OECD countries.Financial development intensifies the environmental benefits of innovation.•The EKC hypothesis is confirmed for all six environmental indicators.
基金supported by SHUFE Graduate Innovation and Creativity Funds(No.2011130151)supported by grants from the National Natural Science Foundation of China(NSFC)(No.11071154)+1 种基金partially supported by the Leading Academic Discipline Program211 Project for Shanghai University of Finance and Economics
文摘This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.
基金National Natural Sciences Foundation of China,Grant/Award Numbers:62125302,62203087Sci-Tech Talent Innovation Support Program of Dalian,Grant/Award Number:2022RG03+1 种基金Liaoning Revitalization Talents Program,Grant/Award Number:XLYC2002087Young Elite Scientist Sponsorship Program by CAST,Grant/Award Number:YESS20220018。
文摘The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.
基金supported in part by the National Natural Science Foundation of China under Grant 62203395in part by the Natural Science Foundation of Henan under Grant 242300421167in part by the China Postdoctoral Science Foundation under Grant 2023TQ0306.
文摘With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).
